

import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DPTImageProcessor



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=7  ,      _lowercase=3  ,      _lowercase=18  ,      _lowercase=30  ,      _lowercase=400  ,      _lowercase=True  ,      _lowercase=None  ,      _lowercase=True  ,      _lowercase=[0.5, 0.5, 0.5]  ,      _lowercase=[0.5, 0.5, 0.5]  ,      )     -> Any:
									a_						:						Union[str, Any] 							= size if size is not None else {"""height""": 18, """width""": 18}
									a_						:						Optional[Any] 							= parent
									a_						:						str 							= batch_size
									a_						:						Tuple 							= num_channels
									a_						:						Any 							= image_size
									a_						:						List[str] 							= min_resolution
									a_						:						Optional[int] 							= max_resolution
									a_						:						Dict 							= do_resize
									a_						:						Tuple 							= size
									a_						:						Optional[int] 							= do_normalize
									a_						:						str 							= image_mean
									a_						:						int 							= image_std





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}







@require_torch
@require_vision
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				List[Any]       					=			DPTImageProcessor if is_vision_available() else None





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						Union[str, Any] 							= DPTImageProcessingTester(self    )





						@property
						def 			UpperCamelCase__					(    self    )     -> str:
									return self.image_processor_tester.prepare_image_processor_dict()





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						str 							= self.image_processing_class(**self.image_processor_dict    )
									self.assertTrue(hasattr(_lowercase  ,      """image_mean"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """image_std"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """do_normalize"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """do_resize"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """size"""    )    )





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						Tuple 							= self.image_processing_class.from_dict(self.image_processor_dict    )
									self.assertEqual(image_processor.size  ,      {"""height""": 18, """width""": 18}    )

									a_						:						int 							= self.image_processing_class.from_dict(self.image_processor_dict  ,      size=42    )
									self.assertEqual(image_processor.size  ,      {"""height""": 42, """width""": 42}    )





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									# Initialize image_processing
									a_						:						int 							= self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									a_						:						List[str] 							= prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=_lowercase    )
									for image in image_inputs:
												self.assertIsInstance(_lowercase  ,      Image.Image    )

									# Test not batched input
									a_						:						Optional[int] 							= image_processing(image_inputs[0]  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )

									# Test batched
									a_						:						Optional[int] 							= image_processing(_lowercase  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									# Initialize image_processing
									a_						:						Tuple 							= self.image_processing_class(**self.image_processor_dict    )
									# create random numpy tensors
									a_						:						List[Any] 							= prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=_lowercase  ,      numpify=_lowercase    )
									for image in image_inputs:
												self.assertIsInstance(_lowercase  ,      np.ndarray    )

									# Test not batched input
									a_						:						Union[str, Any] 							= image_processing(image_inputs[0]  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )

									# Test batched
									a_						:						Optional[int] 							= image_processing(_lowercase  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )





						def 			UpperCamelCase__					(    self    )     -> Dict:
									# Initialize image_processing
									a_						:						str 							= self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									a_						:						Dict 							= prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=_lowercase  ,      torchify=_lowercase    )
									for image in image_inputs:
												self.assertIsInstance(_lowercase  ,      torch.Tensor    )

									# Test not batched input
									a_						:						Any 							= image_processing(image_inputs[0]  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )

									# Test batched
									a_						:						Optional[int] 							= image_processing(_lowercase  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )  ,      )





import math


def 					_UpperCAmelCase				(						a__ = 1_0_0):

			'''simple docstring'''



			a_						:						List[str] 							= sum(i * i for i in range(1  ,	n + 1))
			a_						:						Optional[Any] 							= int(math.pow(sum(range(1  ,	n + 1))  ,	2))
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")


